首页|Tianjin University Reports Findings in Mathematics (Decoding motor imagery loade d on steady-state somatosensory evoked potential based on complex task-related c omponent analysis)
Tianjin University Reports Findings in Mathematics (Decoding motor imagery loade d on steady-state somatosensory evoked potential based on complex task-related c omponent analysis)
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2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Mathematics is the subject of a r eport. According to news reporting from Tianjin, People's Republic of China, by NewsRx journalists, research stated, "Motor Imagery (MI) recognition is one of t he most critical decoding problems in brain- computer interface field. Combined with the steady-state somatosensory evoked potential (MI-SSSEP), this new paradi gm can achieve higher recognition accuracy than the traditional MI paradigm." The news correspondents obtained a quote from the research from Tianjin Universi ty, "Typical algorithms do not fully consider the characteristics of MI-SSSEP si gnals. Developing an algorithm that fully captures the paradigm's characteristic s to reduce false triggering rate is the new step in improving performance. The idea to use complex signal task-related component analysis (cTRCA) algorithm for spatial filtering processing has been proposed in this paper according to the f eatures of SSSEP signal. In this research, it's proved from the analysis of simu lation signals that task-related component analysis (TRCA) as typical method is affected when the response between stimuli has reduced correlation and the propo sed algorithm can effectively overcome this problem. The experimental data under the MI-SSSEP paradigm have been used to identify right-handed target tasks and three unique interference tasks are used to test the false triggering rate. cTRC A demonstrates superior performance as confirmed by the Wilcoxon signed-rank tes t. The recognition algorithm of cTRCA combined with mutual information-based bes t individual feature (MIBIF) and minimum distance to mean (MDM) can obtain AUC v alue up to 0.89, which is much higher than traditional algorithm common spatial pattern (CSP) combined with support vector machine (SVM) (the average AUC value is 0.77, p<0.05). Compared to CSP+SVM, this algorithm mode l reduced the false triggering rate from 38.69 % to 20.74 % (p <0.001). The research prove that TRCA is influenced by MI-SSSEP signals."
TianjinPeople's Republic of ChinaAsi aAlgorithmsMathematics
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